Application of Hyperspectral Imaging to Differentiate COPD and Normal Lung Tissue in Mouse Lung Histopathological Sections

This study investigates the potential of hyperspectral imaging (HSI) as a label‐free, quantitative tool for differentiating chronic obstructive pulmonary disease (COPD) from normal mouse lung tissues based on their intrinsic optical signatures. High‐resolution HSI data (400–1000 nm) were acquired from paraffin‐embedded sections and analyzed using principal component analysis (PCA) for dimensionality reduction, followed by partial least squares–discriminant analysis (PLS‐DA) and support vector machine (SVM) classification. Distinct spectral disparities, particularly in hemoglobin and water‐associated absorption bands, enabled accurate discrimination with classification performance exceeding 93%. These findings establish HSI as a non‐destructive and objective modality capable of capturing subtle pathological alterations in pulmonary tissue, offering a robust experimental and theoretical foundation for its translation into pathological diagnostics and quantitative respiratory disease assessment.